code: def test_network_has_specified_number_of_layers(self):
      num_layers = 5
      network_def = continuous_networks.SACCriticNetwork(
          num_layers=num_layers
      )
      params = network_def.init(
          self.key, self.example_state, self.example_action
      )
  
      # Checks which dense layers are found in the param dict.
      layer_names = {
          x for x in params["params"] if x.startswith("Dense_")
      }
  
      # n hidden layers + 1 output layer.
      self.assertLen(layer_names, num_layers + 1)
  
file_path: dopamine/tests/dopamine/jax/continuous_networks_test.py
imports: from absl.testing import absltest
  from dopamine.jax import continuous_networks
  import jax
  from jax import numpy
label_desc: Use image node_id 3 for calling the SacCriticNetworkTest obj's underlying member method code with example usage: obj.test_network_has_specified_number_of_layers() without return types